Detecting interspecific macroparasite interactions from ecological data: patterns and process

• Published in: Ecology letters Vol. 13; no. 5; pp. 606 - 615
• Main Authors: Fenton, Andy, Viney, Mark E., Lello, Jo
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.05.2010; Blackwell
• Subjects: Abundance; Animal and plant ecology; Animal, plant and microbial ecology; Animals; Biological and medical sciences; co-infection; Data analysis; Ecology; Fundamental and applied biological sciences. Psychology; General aspects; Helminths - physiology; host-parasite dynamics; Host-Parasite Interactions; individual based model; parasite infracommunity; Parasites; Species Specificity; Infection; Host parasite relation; co-infection; Dynamics; host—parasite dynamics; Parasite; Ecology; Individual based model; Interspecific relation; parasite infracommunity
• ISSN: 1461-023X; 1461-0248
• DOI: 10.1111/j.1461-0248.2010.01458.x

Ecology Letters (2010) 13: 606–615 There is great interest in the occurrence and consequences of interspecific interactions among co‐infecting parasites. However, the extent to which interactions occur is unknown, because there are no validated methods for their detection. We developed a model that generated abundance data for two interacting macroparasite (e.g., helminth) species, and challenged the data with various approaches to determine whether they could detect the underlying interactions. Current approaches performed poorly – either suggesting there was no interaction when, in reality, there was a strong interaction occurring, or inferring the presence of an interaction when there was none. We suggest the novel application of a generalized linear mixed modelling (GLMM)‐based approach, which we show to be more reliable than current approaches, even when infection rates of both parasites are correlated (e.g., via a shared transmission route). We suggest that the lack of clarity regarding the presence or absence of interactions in natural systems may be largely attributed to the unreliable nature of existing methods for detecting them. However, application of the GLMM approach may provide a more robust method of detection for these potentially important interspecific interactions from ecological data.